Spatial prediction of immunity gaps during a pandemic to inform decision making: A geostatistical case study of COVID-19 in Dominican Republic.

IF 2.6 4区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Tropical Medicine & International Health Pub Date : 2025-03-12 DOI:10.1111/tmi.14094
Angela Cadavid Restrepo, Beatris Mario Martin, Helen J Mayfield, Cecilia Then Paulino, Michael de St Aubin, William Duke, Petr Jarolim, Timothy Oasan, Emily Zielinski Gutiérrez, Ronald Skewes Ramm, Devan Dumas, Salome Garnier, Marie Caroline Etienne, Farah Peña, Gabriela Abdalla, Beatriz Lopez, Lucia de la Cruz, Bernarda Henriquez, Margaret Baldwin, Adam Kucharski, Benn Sartorius, Eric J Nilles, Colleen L Lau
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引用次数: 0

Abstract

Background: To demonstrate the application and utility of geostatistical modelling to provide comprehensive high-resolution understanding of the population's protective immunity during a pandemic and identify pockets with sub-optimal protection.

Methods: Using data from a national cross-sectional household survey of 6620 individuals in the Dominican Republic (DR) from June to October 2021, we developed and applied geostatistical regression models to estimate and predict Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike (anti-S) antibodies (Ab) seroprevalence at high resolution (1 km) across heterogeneous areas.

Results: Spatial patterns in population immunity to SARS-CoV-2 varied across the DR. In urban areas, a one-unit increase in the number of primary healthcare units per population and 1% increase in the proportion of the population aged under 20 years were associated with higher odds ratios of being anti-S Ab positive of 1.38 (95% confidence interval [CI]: 1.35-1.39) and 1.35 (95% CI: 1.32-1.33), respectively. In rural areas, higher odds of anti-S Ab positivity, 1.45 (95% CI: 1.39-1.51), were observed with increasing temperature in the hottest month (per°C), and 1.51 (95% CI: 1.43-1.60) with increasing precipitation in the wettest month (per mm).

Conclusions: A geostatistical model that integrates contextually important socioeconomic and environmental factors can be used to create robust and reliable predictive maps of immune protection during a pandemic at high spatial resolution and will assist in the identification of highly vulnerable areas.

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来源期刊
Tropical Medicine & International Health
Tropical Medicine & International Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.80
自引率
0.00%
发文量
129
审稿时长
6 months
期刊介绍: Tropical Medicine & International Health is published on behalf of the London School of Hygiene and Tropical Medicine, Swiss Tropical and Public Health Institute, Foundation Tropical Medicine and International Health, Belgian Institute of Tropical Medicine and Bernhard-Nocht-Institute for Tropical Medicine. Tropical Medicine & International Health is the official journal of the Federation of European Societies for Tropical Medicine and International Health (FESTMIH).
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